Prediction of transient emission characteristic from diesel engines based on CNN-GRU model optimized by PSO algorithm

References

• Alcan, G., M. Unel, V. Aran, M. Yilmaz, C. Gurel, and K. Koprubasi. 2019. Predicting NOx emissions in diesel engines via sigmoid NARX models using a new experiment design for combustion identification. Measurement 137:71–81. doi:10.1016/j.measurement.2019.01.037.
   Web of Science ®Google Scholar
• Bie, P., L. Ji, H. Cui, G. Li, S. Liu, Y. Yuan, K. He, and H. Liu. 2022. A review and evaluation of nonroad diesel mobile machinery emission control in China. Journal of Environmental Sciences 123:30–40. doi:10.1016/j.jes.2021.12.041.
   Google Scholar
• Castresana, J., G. Gabiña, L. Martin, A. Basterretxea, and Z. Uriondo. 2022. Marine diesel engine ANN modelling with multiple output for complete engine performance map. Fuel 319:123873. doi:10.1016/j.fuel.2022.123873.
   Web of Science ®Google Scholar
• Castresana, J., G. Gabiña, L. Martin, and Z. Uriondo. 2021. Comparative performance and emissions assessments of a single-cylinder diesel engine using artificial neural network and thermodynamic simulation. Applied Thermal Engineering 185:116343. doi:10.1016/j.applthermaleng.2020.116343.
   Web of Science ®Google Scholar
• Fang, J., Z. Meng, J. Li, Y. Pu, Y. Du, J. Li, Z. Jin, C. Chen, and G. Chase. 2017. The influence of ash on soot deposition and regeneration processes in diesel particular filter. Applied Thermal Engineering 124:633–40. doi:10.1016/j.applthermaleng.2017.06.076.
   Web of Science ®Google Scholar
• Fu, J., R. Yang, X. Li, X. Sun, Y. Li, Z. Liu, Y. Zhang, and B. Sunden. 2022. Application of artificial neural network to forecast engine performance and emissions of a spark ignition engine. Applied Thermal Engineering 201:117749. doi:10.1016/j.applthermaleng.2021.117749.
   Web of Science ®Google Scholar
• Genuer, R., J. Poggi, and C. Tuleau-Malot. 2010. Variable selection using random forests. Pattern Recognition Letters 31 (14):2225–36. doi:10.1016/j.patrec.2010.03.014.
   Web of Science ®Google Scholar
• Gregorutti, B., B. Michel, and P. Saint-Pierre. 2017. Correlation and variable importance in random forests. Statistics and Computing 27 (3):659–78. doi:10.1007/s11222-016-9646-1.
   Web of Science ®Google Scholar
• Guo, X., and J. Mao. 2020. Prediction of Gas Concentration Based on PSO Optimized GRU Neural Network. IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Chongqing, China, 1126–1130. doi:10.1109/ICIBA50161.2020.9277240.
   Google Scholar
• Jia, P., H. Liu, S. Wang, and P. Wang. 2020. Research on a mine gas concentration forecasting model based on a GRU network. Institute of Electrical and Electronics Engineers Access 8:38023–31. doi:10.1109/ACCESS.2020.2975257.
   Google Scholar
• Jiang, K., E. Cao, and L. Wei. 2016. NO x sensor ammonia cross-sensitivity estimation with adaptive unscented Kalman filter for diesel-engine selective catalytic reduction systems. Fuel 165:185–92. doi:10.1016/j.fuel.2015.10.019.
   Web of Science ®Google Scholar
• Joshi, A. 2022. Review of vehicle engine efficiency and emissions. SAE Technical Paper 2022-01-0540. doi:10.4271/2022-01-0540.
   Google Scholar
• Karunamurthy, K., A. A. Janvekar, P. L. Palaniappan, V. Adhitya, T. T. K. Lokeswar, and J. Harish. 2023. Prediction of IC engine performance and emission parameters using machine learning: A review. Journal of Thermal Analysis and Calorimetry 148 (9):3155–77. doi:10.1007/s10973-022-11896-2.
   Web of Science ®Google Scholar
• Kim, T., and S. Cho. 2019. Predicting residential energy consumption using CNN-LSTM neural networks. Energy 182:72–81. doi:10.1016/j.energy.2019.05.230.
   Web of Science ®Google Scholar
• Koç, M. A., and R. Şener. 2021. Prediction of emission and performance characteristics of reactivity-controlled compression ignition engine with the intelligent software based on adaptive neural-fuzzy and neural-network. Journal of Cleaner Production 318:128642. doi:10.1016/j.jclepro.2021.128642.
   Web of Science ®Google Scholar
• Liao, J., J. Hu, P. Chen, L. Zhu, Y. Wu, Z. Cai, H. Wu, and M. Wang. 2024. Prediction of the transient emission characteristics from diesel engine using temporal convolutional networks. Engineering Applications of Artificial Intelligence 127:107227. doi:10.1016/j.engappai.2023.107227.
   Web of Science ®Google Scholar
• Liu, B., J. Hu, F. Yan, R. F. Turkson, and F. Lin. 2016. A novel optimal support vector machine ensemble model for NOX emissions prediction of a diesel engine. Measurement 92:183–92. doi:10.1016/j.measurement.2016.06.015.
   Web of Science ®Google Scholar
• Marini, F., and B. Walczak. 2015. Particle swarm optimization (PSO). A tutorial. Chemometrics and Intelligent Laboratory Systems 149:153–65. doi:10.1016/j.chemolab.2015.08.020.
   Web of Science ®Google Scholar
• Meng, Z., J. Li, J. Fang, J. Tan, Y. Qin, Y. Jiang, Z. Qin, W. Bai, and K. Liang. 2020. Experimental study on regeneration performance and particle emission characteristics of DPF with different inlet transition sections lengths. Fuel 262:116487. doi:10.1016/j.fuel.2019.116487.
   Web of Science ®Google Scholar
• Nickabadi, A., M. M. Ebadzadeh, and R. Safabakhsh. 2011. A novel particle swarm optimization algorithm with adaptive inertia weight. Applied Soft Computing 11 (4):3658–70. doi:10.1016/j.asoc.2011.01.037.
   Web of Science ®Google Scholar
• Planakis, N., G. Papalambrou, N. Kyrtatos, and P. Dimitrakopoulos. 2021. Recurrent and time-delay neural networks as virtual sensors for NOx emissions in Marine diesel powertrains. SAE Technical Paper 2021-01-5042. doi:10.4271/2021-01-5042.
   Google Scholar
• Rahimi Molkdaragh, R., S. Jafarmadar, S. Khalilaria, and H. Soukht Saraee. 2018. Prediction of the performance and exhaust emissions of a compression ignition engine using a wavelet neural network with a stochastic gradient algorithm. Energy 142:1128–38. doi:10.1016/j.energy.2017.09.006.
   Web of Science ®Google Scholar
• Ra, Y., and R. D. Reitz. 2011. A combustion model for IC engine combustion simulations with multi-component fuels. Combustion & Flame 158 (1):69–90. doi:10.1016/j.combustflame.2010.07.019.
   Web of Science ®Google Scholar
• Sajjad, M., Z. A. Khan, A. Ullah, T. Hussain, W. Ullah, M. Y. Lee, and S. W. Baik. 2020. A novel CNN-GRU-Based hybrid approach for short-term residential load forecasting. Institute of Electrical and Electronics Engineers Access 8:143759–68. doi:10.1109/ACCESS.2020.3009537.
   Google Scholar
• Sanjeevannavar, M. B., N. R. Banapurmath, V. D. Kumar, A. M. Sajjan, I. A. Badruddin, C. Vadlamudi, S. Krishnappa, S. Kamangar, R. U. Baig, and T. M. Y. Khan. 2023. Machine learning prediction and optimization of performance and emissions characteristics of IC engine. Sustainability 15 (18):13825. doi:10.3390/su151813825.
   Web of Science ®Google Scholar
• Shin, S., Y. Lee, Y. Lee, J. Park, M. Kim, S. Lee, and K. Min. 2022. Designing a steady-state experimental dataset for predicting transient NOx emissions of diesel engines via deep learning. Expert Systems with Applications 198:116919. doi:10.1016/j.eswa.2022.116919.
   Web of Science ®Google Scholar
• Shin, S., J. Won, and M. Kim. 2023. Comparative research on DNN and LSTM algorithms for soot emission prediction under transient conditions in a diesel engine. Journal of Mechanical Science and Technology 37 (6):3141–50. doi:10.1007/s12206-023-0538-y.
   Web of Science ®Google Scholar
• Silitonga, A. S., H. H. Masjuki, H. C. Ong, A. H. Sebayang, S. Dharma, F. Kusumo, J. Siswantoro, J. Milano, K. Daud, T. M. I. Mahlia, et al. 2018. Evaluation of the engine performance and exhaust emissions of biodiesel-bioethanol-diesel blends using kernel-based extreme learning machine. Energy 159:1075–87. doi:10.1016/j.energy.2018.06.202.
   Web of Science ®Google Scholar
• Uslu, S. 2020. Optimization of diesel engine operating parameters fueled with palm oil-diesel blend: Comparative evaluation between Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Fuel 276:117990. doi:10.1016/j.fuel.2020.117990.
   Web of Science ®Google Scholar
• Van Hung, T., H. H. Alkhamis, A. F. Alrefaei, Y. Sohret, and K. Brindhadevi. 2021. Prediction of emission characteristics of a diesel engine using experimental and artificial neural networks. Applied Nanoscience 13 (1):433–42. doi:10.1007/s13204-021-01781-z.
   Google Scholar
• Veza, I., A. Afzal, M. A. Mujtaba, A. Tuan Hoang, D. Balasubramanian, M. Sekar, I. M. R. Fattah, M. E. M. Soudagar, A. I. EL-Seesy, D. W. Djamari, et al. 2022. Review of artificial neural networks for gasoline, diesel and homogeneous charge compression ignition engine. Alexandria Engineering Journal 61(11):8363–91. doi:10.1016/j.aej.2022.01.072.
   Web of Science ®Google Scholar
• Xie, H., Y. Zhang, Y. He, K. You, B. Fan, D. Yu, B. Lei, and W. Zhang. 2021. Parallel attention-based LSTM for building a prediction model of vehicle emissions using PEMS and OBD. Measurement 185:110074. doi:10.1016/j.measurement.2021.110074.
   Web of Science ®Google Scholar
• Xu, G., W. Shan, Y. Yu, Y. Shan, X. Wu, and Y. Wu. 2021. Advances in emission control of diesel vehicles in China. Journal of Environmental Sciences, 123:15–29.
   Google Scholar
• Yap, W. K., and V. Karri. 2011. ANN virtual sensors for emissions prediction and control. Applied Energy 88 (12):4505–16. doi:10.1016/j.apenergy.2011.05.040.
   Web of Science ®Google Scholar
• Yu, Y., Y. Wang, J. Li, M. Fu, A. N. Shah, and C. He. 2021. A novel deep learning approach to predict the instantaneous NOₓ emissions from diesel engine. Institute of Electrical and Electronics Engineers Access 9:11002–13. doi:10.1109/ACCESS.2021.3050165.
   Google Scholar
• Zhong, C., J. Tan, H. Zuo, X. Wu, S. Wang, and J. Liu. 2021. Synergy effects analysis on CDPF regeneration performance enhancement and NOx concentration reduction of NH3–SCR over Cu–ZSM–5. Energy 230:120814.
   Web of Science ®Google Scholar